Background
- Diffusion MRI (dMRI) measures tissue properties of white matter, which contains long-range connections between different brain regions.
- Brain-behavior models can be used link neuroimaging features and phenotypes.
Question: How do sets of features derived from different dMRI processing methods compare in model accuracy and variability?
Methods
- Diffusion MRI from 1041 Human Connectome Project participants
- Processed into “tract profiles” using pyAFQ (cite) and “local connectome” features using DSI-Studio (cite).
- LASSO models were run on both tract profiles and local connectome.
- Sparse Group LASSO (SGL) models run on only tract profiles.
- Prediction targets were various cognitive phenotypes.
- Models implemented using
R and trained using nested cross-validation and boostrap resampling.
Caption for the picture.
Conclusions (rough wording)
- The selection of model and feature set might not be influential on the accuracy, but may result in less variable, more interpretable models.
- Tract profiles and local connectome have similar accuracies, but the grouping of tract profiles combined with SGL is good*
- Splitting families across the train/test splits is bad practice, but didn’t have a large effect on the outcome.
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Figure 2. This figure needs a title. This is an informative caption, which tells you things about the figure.
References
Created with (Allaire et al. 2024)
Allaire, JJ, Yihui Xie, Christophe Dervieux, Jonathan McPherson, Javier Luraschi, Kevin Ushey, Aron Atkins, et al. 2024.
Rmarkdown: Dynamic Documents for r.
https://github.com/rstudio/rmarkdown.